We focus on identifying derivatives of the CA-Phz cocrystal, with Phz selected as a reference molecule due to its established ability to form polar cocrystals. To identify potential candidate molecules with similar characteristics, we employed fingerprint similarity, a computational technique that evaluates molecular resemblance based on specific structural or functional groups (see 30 Supplementary Information Section 6). This HT screening, conducted at a computational rate of 100 molecules per second, efficiently narrowed the search to molecules with similar hydrogen-bonding patterns or structural arrangements to Phz, thereby optimizing the candidate selection process. The next step involved predicting cocrystal formation, a process primarily governed by intermolecular forces that enable the assembly of diverse molecular building blocks. Since Desiraju 15 introduced the concept of supramolecular synthons in 1995, knowledge-based methodologies have become integral to cocrystal design. This framework explains how specific functional groups within a target molecule interact with complementary groups in potential co-formers, providing valuable guidance for selecting suitable candidates for co-crystallization. Beyond structural considerations, certain physicochemical properties have also been recognized as critical for 20 successful cocrystal formation. Building on these principles and recent advances in ML , we developed a predictive method to assess the likelihood of cocrystal formation. Using data from the CSD, we trained a RF model to classify the likelihood of cocrystal formation between molecular pairs. We extracted molecular graphs and fingerprint vectors from 7655 binary organic cocrystals in the CSD as input for the RF model, and to ensure robustness, we generated a corresponding set of 25 invalid combinations through a link-prediction method. This method assigns a statistical likelihood to cocrystal formation between two components based on network science principles. Additional details on model implementation, dataset generation, model selection, and ensembling procedures are provided in the Supplementary Information Section 7. The RF model demonstrated strong accuracy of 91%, demonstrating the potential of ML to predict multicomponent crystal formation. We focused specifically on 1:1 stoichiometric cocrystals, which are the majority in experimentally reported data and are of particular relevance to this field. Notably, the RF model does not predict stoichiometry but instead evaluates the likelihood of co-crystallization based on molecular inputs. By combining data-driven insights with established chemical principles, this approach serves as a robust tool for predicting cocrystal formation and refining candidate selection.