Computational polymorph prediction can complement experiments to de-risk unexpected polymorphic changes during drug development. Unlike experiments, computational methods, in principle, can enable identification of all low energy polymorphs of the API, including those that may not be easily accessible by conventional experimental methods, or that may only appear under specific isolation conditions. This can help avert discovery of new polymorphs in late stage development that could potentially affect the quality, efficacy, and safety of the drug product. Motivated by the crystal structure prediction (CSP) blind test challenge organized by CCDC, the field of computational polymorph prediction has made large leaps in the past two decades. Several studies have demonstrated the ability of computational methods to accurately predict the crystal structures of small molecules, including flexible molecules of comparable complexity to typical modern small molecule drugs. These studies have provided valuable insights and examples for the field of computational polymorph screening. However, most of these previous CSP studies have only investigated a small number of molecules to demonstrate the potential of such calculations. A large-scale validation of the proposed methods for general small molecule crystal structure prediction has yet to be reported.