We have presented the use of QRNN, a machine learned force field (MLFF), to perform structural relaxation and energy ranking of molecular crystals in our CSP workflow prior to DFT. Innovations in the successful use of machine learning methods were also demonstrated by participants in the 7th CSP Blind Test. MLFFs were developed and utilized in the CSP protocols of groups 12, 15, 16, and 23. In the second part of the seventh blind test, Group 16 demonstrated energy ranking results with system specific MLFFs comparable to accurate DFT methods. Additionally, groups 10 and 20 made use of machine learning to reduce the number of structures required to be evaluated by costly methods, such as DFT. Future developments of QRNN in our CSP workflow will include improving molecule specific fine-tuning protocols to further reduce the cost of DFT evaluations.