The landscape of High Performance Computing (HPC) system architectures keeps expanding with new technologies and increased complexity. With the goal of improving the efficiency of next-generation large HPC systems, designers require tools for analyzing and predicting the impact of new architectural features on the performance of complex scientific applications at scale. We simulate five hybrid (MPI+OpenMP) applications over 864 architectural proposals based on stateof-the-art and emerging HPC technologies, relevant both in industry and research. This paper significantly extends our previous work with MUltiscale Simulation Approach (MUSA) enabling accurate performance and power estimations of largescale HPC systems. We reveal that several applications present critical scalability issues mostly due to the software parallelization approach. Looking at speedup and energy consumption exploring the design space (i.e., changing memory bandwidth, number of cores, and type of cores), we provide evidence-based architectural recommendations that will serve as hardware and software codesign guidelines.