This chapter highlights on how computational approaches currently are used at AstraZeneca for solid-state and property predictions. It also highlights a few important areas within pharmaceutical development where predictive science can play an important role for making scientifically informed project decisions. The chapter presents examples on the usefulness of predicting risks for polymorph formation, and some of the underlying tools including the CCDC tools behind current approaches in the field. It highlights a drug project in which structural information extracted from crystal structure of the ligand was used to improve solubility. Some discussed case studies such as the AZD8329 Case Study, illustrate where CSPs have been used in drug projects and on the direct impact of such studies, and how they can be used in connection to an early discovery phase, or at a later phase for helping determining the right form of a drug candidate.