For decades, genetic screen has been central in identifying functional genes and beyond. Traditional genetic screen typically requires a large-scale experimental setup, and is limited by the available resource and experimental feasibility. In the 3D genome organization field, experimental methods, such as Hi-C, are generally costly and bear strong technical limitations, thus restricting their widespread application particularly in high-throughput genetic screen for biological discoveries. To solve this challenge, we recently proposed and developed in silico genetic screen (ISGS) research framework as the next-generation approach to genetic discoveries. Similar to the experimental genetic screen, the ISGS framework interrogates the effect of genetic perturbation through accurate computational modeling in an ultra-high-throughput scenario. To demonstrate the ISGS framework in the genome organization field, we developed C.Origami, a deep neural network that performs accurate de novo prediction of cell type-specific chromatin organization. C.Origami enables high-throughput ISGS to discover regulatory DNA elements on chromatin organization. Applying this approach to leukemia cells and normal T cells, we demonstrate that cell-type-specific in silico genetic screen, enabled by C.Origami, can be used to systematically discover novel chromatin regulation circuits in both normal and disease-related biological systems.