Quantifying a node's importance is decisive for developing efficient strategies to curb or accelerate any spreading phenomena. Centrality measures are well-known methods used to quantify the influence of nodes by extracting information from the network's structure. The pitfall of these measures is to pinpoint nodes located in the vicinity of each other, saturating their shared zone of influence. In this paper, we propose a ranking strategy exploiting the ubiquity of the community structure in real-world networks. The proposed community-aware ranking strategy naturally selects a set of distant spreaders with the most significant influence in the networks. One can use it with any centrality measure. We investigate its effectiveness using real-world and synthetic networks with controlled parameters in a Susceptible-Infected-Recovered (SIR) diffusion model scenario. Experimental results indicate the superiority of the proposed ranking strategy over all its counterparts agnostic about the community structure. Additionally, results show that it performs better in networks with a strong community structure and a high number of communities of heterogeneous sizes.