A new algorithm called Fast and Flexible CrystAl Structure Predictor (FFCASP) was developed to predict the structure of covalent and molecular crystals. FFCASP is massively parallel and able to handle more than 200 atoms in the unit cell (in other terms, it allows global optimization around 100 individual parameters). It uses a global optimizer specialized for Crystal Structure Prediction (CSP) which combines particle swarm and simulated annealing optimizers. Three different molecular crystals, including diverse intermolecular interactions, namely, cytosine, coumarin, and pyrazinamide, have been selected to evaluate the performance of FFCASP. While cytosine polymorphs have been searched by employing two different force fields (a DFT-SAPT based intermolecular potential and generalized amber force field (GAFF)) up to Z = 16, only GAFF has been used both in coumarin and pyrazinamide polymorph searches up to Z = 4. For these three molecular crystals, FFCASP generated more than 20 000 crystal structures, and the unique ones have been further treated by DFT-D3. A combination of data mining and a machine learning approach was introduced to determine the unique structures and their distribution into different clusters, which ultimately gives an opportunity to retrieve the common features and relations between the resulting structures. There are two known experimental crystal structures of cytosine, and both were successfully located with FFCASP. Two of the reported crystal structures of coumarin have been reproduced. Similarly, in pyrazinamide, three known experimental structures have been rediscovered. In addition to finding the experimentally known structures, FFCASP also located other low-energy structures for each considered molecular crystals. These successes of FFCASP offer the possibility to discover the polymorphic nature of other important molecular crystals (e.g., drugs) as well.